Complex partial seizures represent a major seizure type, one which is frequently medically intractable and disabling as a result of impaired consciousness. Specific electrophysiologic characteristics of these seizures include a period of rhythmic activity undergoing a monotonic decline in frequency followed by a period of intermittent bursting before seizure termination. Is not clear why these seizures undergo these dynamic changes. The long-term goal of this research is to use neuronal network models based on the physiology of intrinsic neuronal properties and synaptic connectivity to explain seizure dynamics and evolution. The proposed investigations will address the hypothesis that intense calcium influxes during seizures alter calcium clearance systems, which leads to changes in neuronal excitability and synaptic facilitation. Changes in neuronal excitability are reflected in decline in frequency of rhythmic activity while synaptic facilitation produces intermittent bursting. Using large neural network models of single compartment neurons, investigations will determine how changing [Ca2+]i and effects on calcium-mediated after hyperpolarization and synaptic facilitation influence the spatiotemporal characteristics of simulated seizure patterns. The results of computational studies will be compared with results of clinical studies. Identifying mechanisms underlying the dynamic changes seen in ictal intracranial EEG can yield important insights into understanding of seizure evolution and termination. This can facilitate development of technology for controlling or reducing the frequency of seizures and lead to possibilities for developing new treatment options for patients with epilepsy. [unreadable] [unreadable] [unreadable]